Implement other code examples on Predictive Models which detect anomalies using MAPE/MSE value

**We have already implemented one code sample which uses fbprophet to detect anomalies**
We would want you to make more code examples using other predictive models. :pray:

**List of Predictive Models that can be used**

- [x] ARIMA

- [x] SARIMA

- [x] ARCH

- [ ] GARCH

- [ ] VAR

- [ ] VECM

- [ ] LSTM

- [ ] Convolutional Autoencoder model

**Instructions to be followed**
* The Code examples on the implementations of most of the models mentioned above should be available in the internet. :octocat:
* Build the respective predictive model on the NAB Dataset(RealAWSCloudwatch - Any CPU Utilization Data ONLY).
* Use the Predictive Model to forecast future values.
* Find the Error rate of the predictive (Use MAPE/MSE) and come up with a confidence interval for the predicted value.
* Any Actual datapoint falling beyond the confidence interval, will be detected as an anomaly.

**Additional instructions**
* Make a new jupyter notebook with the algorithm name and work in that only.
* Initially above algorithms will be assigned on a First Come First Serve basis. The algorithm will be assigned to you if you create an "assign request" comment below with a small description of the algorithm you have requested for. :smiley: :1st_place_medal:

@JoeRishon & @manikyabard for any doubts and additional details